title: Analysis of time course Omics datasets.
abstract: Over the past 20 years, Omics technologies emerged as the consensual denomination of holistic molecular profiling. These techniques enable parallel measurements of biological -omes, or "all constituents considered collectively", and utilize the latest advancements in transcriptomics, proteomics, metabolomics, imaging, and bioinformatics. The technological accomplishments in increasing the sensitivity and throughput of the analytical devices, the standardization of the protocols and the widespread availability of reagents made the capturing of static molecular portraits of biological systems a routine task. The next generation of time course molecular profiling already allows for extensive molecular snapshots to be taken along the trajectory of time evolution of the investigated biological systems. Such datasets provide the basis for application of the inverse scientific approach. It consists in the inference of scientific hypotheses and theories about the structure and dynamics of the investigated biological system without any a priori knowledge, solely relying on data analysis to unveil the underlying patterns. However, most temporal Omics data still contain a limited number of time points, taken over arbitrary time intervals, through measurements on biological processes shifted in time. The analysis of the resulting short and noisy time series data sets is a challenge. Traditional statistical methods for the study of static Omics datasets are of limited relevance and new methods are required. This chapter discusses such algorithms which enable the application of the inverse analysis approach to short Omics time series.
authors.affiliation: Nestlé Research Center, Lausanne, Switzerland. martin.grigorov@rdls.nestle.com
authors.forename: Martin G
authors.initials: MG
authors.lastname: Grigorov
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D019451:Information Management; D015999:Multivariate Analysis; D025341:Principal Component Analysis; D013223:Statistics as Topic / Q000379:methods*; D013997:Time Factors